{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brain tumor 3D segmentation with MONAI\n",
    "\n",
    "This tutorial shows how to construct a training workflow of multi-labels segmentation task.\n",
    "\n",
    "And it contains below features:\n",
    "1. Transforms for dictionary format data.\n",
    "1. Define a new transform according to MONAI transform API.\n",
    "1. Load Nifti image with metadata, load a list of images and stack them.\n",
    "1. Randomly adjust intensity for data augmentation.\n",
    "1. Cache IO and transforms to accelerate training and validation.\n",
    "1. 3D UNet model, Dice loss function, Mean Dice metric for 3D segmentation task.\n",
    "1. Deterministic training for reproducibility.\n",
    "\n",
    "The dataset comes from http://medicaldecathlon.com/.  \n",
    "Target: Gliomas segmentation necrotic/active tumour and oedema  \n",
    "Modality: Multimodal multisite MRI data (FLAIR, T1w, T1gd,T2w)  \n",
    "Size: 750 4D volumes (484 Training + 266 Testing)  \n",
    "Source: BRATS 2016 and 2017 datasets.  \n",
    "Challenge: Complex and heterogeneously-located targets\n",
    "\n",
    "Below figure shows image patches with the tumor sub-regions that are annotated in the different modalities (top left) and the final labels for the whole dataset (right).\n",
    "(Figure taken from the [BraTS IEEE TMI paper](https://ieeexplore.ieee.org/document/6975210/))\n",
    "\n",
    "![image](./images/brats_tasks.png)\n",
    "\n",
    "The image patches show from left to right:\n",
    "1. the whole tumor (yellow) visible in T2-FLAIR (Fig.A).\n",
    "1. the tumor core (red) visible in T2 (Fig.B).\n",
    "1. the enhancing tumor structures (light blue) visible in T1Gd, surrounding the cystic/necrotic components of the core (green) (Fig. C).\n",
    "1. The segmentations are combined to generate the final labels of the tumor sub-regions (Fig.D): edema (yellow), non-enhancing solid core (red), necrotic/cystic core (green), enhancing core (blue).\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/MONAI/blob/master/examples/notebooks/brats_segmentation_3d.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -qU \"monai[gdown, nibabel]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -qU matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "\n",
    "import IPython\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "from monai.apps import DecathlonDataset\n",
    "from monai.config import print_config\n",
    "from monai.data import DataLoader\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.networks.nets import UNet\n",
    "from monai.transforms import (\n",
    "    AsChannelFirstd,\n",
    "    CenterSpatialCropd,\n",
    "    Compose,\n",
    "    LoadNiftid,\n",
    "    MapTransform,\n",
    "    NormalizeIntensityd,\n",
    "    Orientationd,\n",
    "    RandFlipd,\n",
    "    RandScaleIntensityd,\n",
    "    RandShiftIntensityd,\n",
    "    RandSpatialCropd,\n",
    "    Spacingd,\n",
    "    ToTensord,\n",
    ")\n",
    "from monai.utils import set_determinism\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a new transform to convert brain tumor labels\n",
    "\n",
    "Here we convert the multi-classes labels into multi-labels segmentation task in One-Hot format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):\n",
    "    \"\"\"\n",
    "    Convert labels to multi channels based on brats classes:\n",
    "    label 1 is the peritumoral edema\n",
    "    label 2 is the GD-enhancing tumor\n",
    "    label 3 is the necrotic and non-enhancing tumor core\n",
    "    The possible classes are TC (Tumor core), WC (Whole tumor)\n",
    "    and ET (Enhancing tumor).\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __call__(self, data):\n",
    "        d = dict(data)\n",
    "        for key in self.keys:\n",
    "            result = list()\n",
    "            # merge label 2 and label 3 to construct TC\n",
    "            result.append(np.logical_or(d[key] == 2, d[key] == 3))\n",
    "            # merge labels 1, 2 and 3 to construct WC\n",
    "            result.append(np.logical_or(np.logical_or(d[key] == 2, d[key] == 3), d[key] == 1))\n",
    "            # label 2 is ET\n",
    "            result.append(d[key] == 2)\n",
    "            d[key] = np.stack(result, axis=0).astype(np.float32)\n",
    "        return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transform = Compose(\n",
    "    [\n",
    "        # load 4 Nifti images and stack them together\n",
    "        LoadNiftid(keys=[\"image\", \"label\"]),\n",
    "        AsChannelFirstd(keys=\"image\"),\n",
    "        ConvertToMultiChannelBasedOnBratsClassesd(keys=\"label\"),\n",
    "        Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "        RandSpatialCropd(keys=[\"image\", \"label\"], roi_size=[128, 128, 64], random_size=False),\n",
    "        RandFlipd(keys=[\"image\", \"label\"], prob=0.5, spatial_axis=0),\n",
    "        NormalizeIntensityd(keys=\"image\", nonzero=True, channel_wise=True),\n",
    "        RandScaleIntensityd(keys=\"image\", factors=0.1, prob=0.5),\n",
    "        RandShiftIntensityd(keys=\"image\", offsets=0.1, prob=0.5),\n",
    "        ToTensord(keys=[\"image\", \"label\"]),\n",
    "    ]\n",
    ")\n",
    "val_transform = Compose(\n",
    "    [\n",
    "        LoadNiftid(keys=[\"image\", \"label\"]),\n",
    "        AsChannelFirstd(keys=\"image\"),\n",
    "        ConvertToMultiChannelBasedOnBratsClassesd(keys=\"label\"),\n",
    "        Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "        CenterSpatialCropd(keys=[\"image\", \"label\"], roi_size=[128, 128, 64]),\n",
    "        NormalizeIntensityd(keys=\"image\", nonzero=True, channel_wise=True),\n",
    "        ToTensord(keys=[\"image\", \"label\"]),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quickly load data with DecathlonDataset\n",
    "\n",
    "Here we use `DecathlonDataset` to automatically download and extract the dataset.\n",
    "It inherits MONAI `CacheDataset`, so we set `cache_num=100` to cache 100 items for training and use the defaut args to cache all the items for validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_ds = DecathlonDataset(\n",
    "    root_dir=root_dir,\n",
    "    task=\"Task01_BrainTumour\",\n",
    "    transform=train_transform,\n",
    "    section=\"training\",\n",
    "    download=True,\n",
    "    num_workers=4,\n",
    "    cache_num=100,\n",
    ")\n",
    "train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4)\n",
    "val_ds = DecathlonDataset(\n",
    "    root_dir=root_dir,\n",
    "    task=\"Task01_BrainTumour\",\n",
    "    transform=val_transform,\n",
    "    section=\"validation\",\n",
    "    download=False,\n",
    "    num_workers=4,\n",
    ")\n",
    "val_loader = DataLoader(val_ds, batch_size=2, shuffle=False, num_workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check data shape and visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: torch.Size([4, 128, 128, 64])\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label shape: torch.Size([3, 128, 128, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pick one image from DecathlonDataset to visualize and check the 4 channels\n",
    "print(f\"image shape: {val_ds[9]['image'].shape}\")\n",
    "plt.figure(\"image\", (24, 6))\n",
    "for i in range(4):\n",
    "    plt.subplot(1, 4, i + 1)\n",
    "    plt.title(f\"image channel {i}\")\n",
    "    plt.imshow(val_ds[9][\"image\"][i, :, :, 20].detach().cpu(), cmap=\"gray\")\n",
    "plt.show()\n",
    "# also visualize the 3 channels label corresponding to this image\n",
    "print(f\"label shape: {val_ds[9]['label'].shape}\")\n",
    "plt.figure(\"label\", (18, 6))\n",
    "for i in range(3):\n",
    "    plt.subplot(1, 3, i + 1)\n",
    "    plt.title(f\"label channel {i}\")\n",
    "    plt.imshow(val_ds[9][\"label\"][i, :, :, 20].detach().cpu())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Model, Loss, Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n",
    "device = torch.device(\"cuda:0\")\n",
    "model = UNet(\n",
    "    dimensions=3,\n",
    "    in_channels=4,\n",
    "    out_channels=3,\n",
    "    channels=(16, 32, 64, 128, 256),\n",
    "    strides=(2, 2, 2, 2),\n",
    "    num_res_units=2,\n",
    ").to(device)\n",
    "loss_function = DiceLoss(to_onehot_y=False, sigmoid=True, squared_pred=True)\n",
    "optimizer = torch.optim.Adam(model.parameters(), 1e-4, weight_decay=1e-5, amsgrad=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute a typical PyTorch training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "epoch_num = 180\n",
    "val_interval = 2\n",
    "best_metric = -1\n",
    "best_metric_epoch = -1\n",
    "epoch_loss_values = list()\n",
    "metric_values = list()\n",
    "metric_values_tc = list()\n",
    "metric_values_wt = list()\n",
    "metric_values_et = list()\n",
    "\n",
    "for epoch in range(epoch_num):\n",
    "    print(\"-\" * 10)\n",
    "    print(f\"epoch {epoch + 1}/{epoch_num}\")\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    step = 0\n",
    "    for batch_data in train_loader:\n",
    "        step += 1\n",
    "        inputs, labels = (\n",
    "            batch_data[\"image\"].to(device),\n",
    "            batch_data[\"label\"].to(device),\n",
    "        )\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = loss_function(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        print(f\"{step}/{len(train_ds) // train_loader.batch_size}, train_loss: {loss.item():.4f}\")\n",
    "    epoch_loss /= step\n",
    "    epoch_loss_values.append(epoch_loss)\n",
    "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            dice_metric = DiceMetric(include_background=True, sigmoid=True, reduction=\"mean\")\n",
    "            metric_sum = metric_sum_tc = metric_sum_wt = metric_sum_et = 0.0\n",
    "            metric_count = metric_count_tc = metric_count_wt = metric_count_et = 0\n",
    "            for val_data in val_loader:\n",
    "                val_inputs, val_labels = (\n",
    "                    val_data[\"image\"].to(device),\n",
    "                    val_data[\"label\"].to(device),\n",
    "                )\n",
    "                val_outputs = model(val_inputs)\n",
    "                # compute overall mean dice\n",
    "                value = dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "                not_nans = dice_metric.not_nans.item()\n",
    "                metric_count += not_nans\n",
    "                metric_sum += value.item() * not_nans\n",
    "                # compute mean dice for TC\n",
    "                value_tc = dice_metric(y_pred=val_outputs[:, 0:1], y=val_labels[:, 0:1])\n",
    "                not_nans = dice_metric.not_nans.item()\n",
    "                metric_count_tc += not_nans\n",
    "                metric_sum_tc += value_tc.item() * not_nans\n",
    "                # compute mean dice for WT\n",
    "                value_wt = dice_metric(y_pred=val_outputs[:, 1:2], y=val_labels[:, 1:2])\n",
    "                not_nans = dice_metric.not_nans.item()\n",
    "                metric_count_wt += not_nans\n",
    "                metric_sum_wt += value_wt.item() * not_nans\n",
    "                # compute mean dice for ET\n",
    "                value_et = dice_metric(y_pred=val_outputs[:, 2:3], y=val_labels[:, 2:3])\n",
    "                not_nans = dice_metric.not_nans.item()\n",
    "                metric_count_et += not_nans\n",
    "                metric_sum_et += value_et.item() * not_nans\n",
    "\n",
    "            metric = metric_sum / metric_count\n",
    "            metric_values.append(metric)\n",
    "            metric_tc = metric_sum_tc / metric_count_tc\n",
    "            metric_values_tc.append(metric_tc)\n",
    "            metric_wt = metric_sum_wt / metric_count_wt\n",
    "            metric_values_wt.append(metric_wt)\n",
    "            metric_et = metric_sum_et / metric_count_et\n",
    "            metric_values_et.append(metric_et)\n",
    "            if metric > best_metric:\n",
    "                best_metric = metric\n",
    "                best_metric_epoch = epoch + 1\n",
    "                torch.save(model.state_dict(), os.path.join(root_dir, \"best_metric_model.pth\"))\n",
    "                print(\"saved new best metric model\")\n",
    "            print(\n",
    "                f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "                f\" tc: {metric_tc:.4f} wt: {metric_wt:.4f} et: {metric_et:.4f}\"\n",
    "                f\"\\nbest mean dice: {best_metric:.4f} at epoch: {best_metric_epoch}\"\n",
    "            )\n",
    "\n",
    "    IPython.display.clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train completed, best_metric: 0.7537  at epoch: 160\n"
     ]
    }
   ],
   "source": [
    "print(f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the loss and metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Val Mean Dice\")\n",
    "x = [val_interval * (i + 1) for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(\"train\", (18, 6))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title(\"Val Mean Dice TC\")\n",
    "x = [val_interval * (i + 1) for i in range(len(metric_values_tc))]\n",
    "y = metric_values_tc\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(x, y, color=\"blue\")\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title(\"Val Mean Dice WT\")\n",
    "x = [val_interval * (i + 1) for i in range(len(metric_values_wt))]\n",
    "y = metric_values_wt\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(x, y, color=\"brown\")\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title(\"Val Mean Dice ET\")\n",
    "x = [val_interval * (i + 1) for i in range(len(metric_values_et))]\n",
    "y = metric_values_et\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.plot(x, y, color=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check best model output with the input image and label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(os.path.join(root_dir, \"best_metric_model.pth\")))\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    # select one image to evaluate and visualize the model output\n",
    "    val_input = val_ds[6][\"image\"].unsqueeze(0).to(device)\n",
    "    val_output = model(val_input)\n",
    "    plt.figure(\"image\", (24, 6))\n",
    "    for i in range(4):\n",
    "        plt.subplot(1, 4, i + 1)\n",
    "        plt.title(f\"image channel {i}\")\n",
    "        plt.imshow(val_ds[6][\"image\"][i, :, :, 20].detach().cpu(), cmap=\"gray\")\n",
    "    plt.show()\n",
    "    # visualize the 3 channels label corresponding to this image\n",
    "    plt.figure(\"label\", (18, 6))\n",
    "    for i in range(3):\n",
    "        plt.subplot(1, 3, i + 1)\n",
    "        plt.title(f\"label channel {i}\")\n",
    "        plt.imshow(val_ds[6][\"label\"][i, :, :, 20].detach().cpu())\n",
    "    plt.show()\n",
    "    # visualize the 3 channels model output corresponding to this image\n",
    "    plt.figure(\"output\", (18, 6))\n",
    "    for i in range(3):\n",
    "        plt.subplot(1, 3, i + 1)\n",
    "        plt.title(f\"output channel {i}\")\n",
    "        plt.imshow((val_output[0, i, :, :, 20].sigmoid() >= 0.5).float().detach().cpu())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
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